| Event | Start | End | Length | Mean I | Max I | Mean Z | Mean KE | Cum P | NP | D50 |
|---|---|---|---|---|---|---|---|---|---|---|
| 365 p1 | 25/11/2014 23:25 | 26/11/2014 03:04 | 220 | 11.31 | 240.51 | 28.4 | 10.6 | 40.6 | 415941 | 0.444 |
| 365 p2 | 25/11/2014 23:25 | 26/11/2014 03:04 | 220 | 11.32 | 198.90 | 31.8 | 13.5 | 39.6 | 460871 | 0.448 |
| 365 p3 | 25/11/2014 23:25 | 26/11/2014 03:04 | 220 | 6.24 | 111.00 | 27.1 | 13.1 | 21.8 | 83125 | 0.856 |
| 365 p4 | 25/11/2014 23:25 | 26/11/2014 03:04 | 220 | 5.70 | 96.43 | 27.1 | 12.7 | 21.0 | 72017 | 0.880 |
# define start and finish times
a <-as.POSIXct(strptime("2014-11-25 23:30:00","%Y-%m-%d %H:%M:%S"))
b <-as.POSIXct(strptime("2014-11-26 04:00:00","%Y-%m-%d %H:%M:%S"))
times <- seq(a, b, by="min")Read the raw PSVD data, using function psvd_read from the disdRo package.
# Thies 1
files <- list.files('./p1','.txt$|.txt.gz$', full.names=TRUE, recursive=TRUE)
dsd1 <- psvd_read(files, type='Thies')
dsd1 <- apply(dsd1, c(2,3), sum)
# Thies 2
files <- list.files('./p2','.txt$|.txt.gz$', full.names=TRUE, recursive=TRUE)
dsd2 <- psvd_read(files, type='Thies')
dsd2 <- apply(dsd2, c(2,3), sum)
# Parsivel 1
files <- list.files('./p3','.txt$|.txt.gz$', full.names=TRUE, recursive=TRUE)
dsd3 <- psvd_read(files, type='Parsivel')
dsd3 <- apply(dsd3, c(2,3), sum)
# Parsivel 2
files <- list.files('./p4','.txt$|.txt.gz$', full.names=TRUE, recursive=TRUE)
dsd4 <- psvd_read(files, type='Parsivel')
dsd4 <- apply(dsd4, c(2,3), sum)Particle size and velocity plots, using function psvd_plot from the disdRo package. First, we shall plot the raw data as read from the disdrometer telegrams, and then we shall apply a filter to the PSVD matrix based on a 50% difference with respect to the fall velocity model of Beard (1976).
We first create a filter using the function psvd_filter from the disdRo package. We limit the data to particle sizes between 0.25 and 8 mm. We use a discrepancy threshold of 50% with respect to the Beard (1976) model, setting the elevation (alt) to 230 m to account for the elevation effect on the fall velocity of raindrops.
There will be two filters, one for Thies disdrometers (fltT) and one for Parsivel ones (fltP).
# Thies
fltT <- psvd_filter(type='Thies', d=c(0.25,8), tau=0.5, alt=230)
image(fltT)# Parsivel
fltP <- psvd_filter(type='Parsivel', d=c(0.25,8), tau=0.5, alt=230)
image(fltP)We now do the plots. Transparency of the filtered area can be set with parameter alpha.
We now produce particle size distribution plots, using the function psd_plot from the disdRo package. These plots depict the particle density (ND), against the particle size class.
We now produce particle fall velocity distribution plots, using the function psd_plot from the disdRo package. These plots depict the particle density (ND), against the particle size class.